---
title: "MASTER: A Multi-granularity Invariant Structure Clustering Scheme for Multi-view Clustering"
authors:
  - "Suixue Wang"
  - "Shilin Zhang"
  - "Qingchen Zhang"
  - "Peng Li"
  - "Weiliang Huo"
doi: "10.24963/ijcai.2025/714"
url: "https://ijcai.org/proceedings/2025/714"
pdf: "https://ijcai.org/proceedings/2025/0714.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "6415-6423"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Clustering"
  - "Machine Learning: ML: Multi-view learning"
---

# MASTER: A Multi-granularity Invariant Structure Clustering Scheme for Multi-view Clustering

Suixue Wang, Shilin Zhang, Qingchen Zhang, Peng Li, Weiliang Huo

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 6415-6423. DOI: [10.24963/ijcai.2025/714](https://doi.org/10.24963/ijcai.2025/714)

## Abstract

Deep multi-view clustering has attracted increasing attention in the pattern mining of data. However, most of them perform self-learning mechanisms in a single space, ignoring the fruitful structural information hidden in different-level feature spaces. Meanwhile, they conduct the reconstruction constraint to learn generalized representations of samples, failing to explore the discriminative ability of complementary and consistent information. To address the challenges, a multi-granularity invariant structure clustering scheme (MASTER) is proposed to define a bottom-up process that extracts multi-level information in sample, neighborhood, and category granularities from low-level, high-level, and semantics feature space, respectively. Specifically, it leverages the self-learning reconstruction with information-theoretic overclustering to capture invariant sample structure in the low-level feature space. Then, it models data diffusion of the clustering process in the reliable neighborhood to capture invariant local structure in the high-level feature space. Meanwhile, it defines dual divergences induced by the space geometry to capture invariant global structure in the semantics space. Finally, extensive experiments on 8 real-world datasets show that MASTER achieves state-of-the-art performance compared to 11 baselines.